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39 - Current and Emerging Technologies for Supporting Successful Aging

from Part V - Later Life and Interventions

Published online by Cambridge University Press:  28 May 2020

By
Wendy A. Rogers ,
Kenneth A. Blocker  and
Lucile Dupuy
Edited by
Ayanna K. Thomas  and
Angela Gutchess
Ayanna K. Thomas
Affiliation:
Tufts University, Massachusetts
Angela Gutchess
Affiliation:
Brandeis University, Massachusetts
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Summary

Successful aging can be generally defined as minimizing disabilities, maintaining functional capacity, and supporting an engaged lifestyle. Given world population changes, this concept is of increasing importance. Technologies have become an integral part of daily life across a range of domains and have potential to support older adults. However, for that potential to be met, the technology must be designed with consideration for older adults’ capabilities, limitations, motivations to use technological support, and opinions regarding the role of technology in their lives. In this context, we review the theoretical background relevant to successful aging and technology support. Moreover, the technology should not impose cognitive demands but should augment or enhance cognitive function. We present older adult personas to highlight how current and emerging technologies can assist aging individuals in meeting their diverse needs and reaching their goals. We provide considerations and future research directions to guide technology design and promote successful aging.

Keywords

selection optimization compensation (SOC) modeltechnologyself-determination theoryPERMAUTAUT2senior acceptance model (STAM)continued care retirement communities
Type
Chapter
Information
The Cambridge Handbook of Cognitive Aging
A Life Course Perspective
 , pp. 717 - 736
Publisher: Cambridge University Press
Print publication year: 2020

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References

Anderson, M. (2015). Technology device ownership. Washington: Pew Research Center.Google Scholar
Anderson, M., & Perrin, A. (2017). Tech adoption climbs among older adults. Washington: Pew Research Center.Google Scholar
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010Google Scholar
Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In Baltes, P. B. & Baltes, M. M. (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). New York: Cambridge University Press.Google Scholar
Berkowsky, R. W., Sharit, J., & Czaja, S. J. (2018). Factors predicting decisions about technology adoption among older adults. Innovation in Aging, 1(3), 1–12. https://doi.org/10.1093/geroni/igy002Google Scholar
Blackman, S., Matlo, C., Bobrovitskiy, C., et al. (2016). Ambient assisted living technologies for aging well: A scoping review. Journal of Intelligent Systems, 25(1), 55–69. https://doi.org/10.1515/jisys-2014-0136Google Scholar
Blocker, K. A., Insel, K. C., Lee, J. K., et al. (2018). User insights for design of an antihypertensive medication management application. In Proceedings of the Human Factors and Ergonomics Society 62nd Annual Meeting (pp. 1077–1081). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
Brooke, J. (1996). SUS – A quick and dirty usability scale. Usability Evaluation in Industry, 189(194), 4–7.Google Scholar
Chen, K., & Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: A senior technology acceptance model (STAM). Ergonomics, 57(5), 635–652. https://doi.org/10.1080/00140139.2014.895855Google Scholar
Chin, J., Moeller, D. D., Johnson, J., et al. (2018). A multi-faceted approach to promote comprehension of online health information among older adults. Gerontologist, 58, 686–695. https://doi.org/10.1093/geront/gnw254Google Scholar
Confente, I., & Vigolo, V. (2018). Online travel behaviour across cohorts: The impact of social influences and attitude on hotel booking intention. International Journal of Tourism Research, 20(5), 660–670. https://doi.org/10.1002/jtr.2214Google Scholar
Consel, C. (2018). Assistive computing: A human-centered approach to developing computing support for cognition. In ICSE-SEIS’18: 40th International Conference on Software Engineering: Software Track (pp. 23–32). New York: ACM.Google Scholar
Consel, C., Dupuy, L., & Sauzéon, H. (2017, July). HomeAssist: An assisted living platform for aging in place based on an interdisciplinary approach. In International Conference on Applied Human Factors and Ergonomics (pp. 129–140). Cham, Switzerland: Springer.Google Scholar
Cornejo, R., Favela, J., & Tentori, M. (2010). Ambient displays for integrating older adults into social networking sites. In International Conference on Collaboration and Technology (pp. 321–336). Berlin: Springer.Google Scholar
Cotten, S. R., Yost, E., Berkowsky, R. W., Winstead, V., & Anderson, W. A. (2016). Designing technology training for older adults in continuing care retirement communities. Boca Raton, FL: CRC Press.Google Scholar
Craik, F. I. M. (1986). A functional account of age differences in memory. In Klix, F. & Hagendorf, H. (Eds.), Human memory and cognitive capabilities, mechanisms, and performances (pp. 409–422). Amsterdam: Elsevier Science.Google Scholar
Czaja, S. J., Boot, W. R., Charness, N., & Rogers, W. A. (2019). Designing for older adults: Principles and creative human factors approaches, 3rd ed. Boca Raton, FL: CRC PressGoogle Scholar
Czaja, S. J., Boot, W. R., Charness, N., Rogers, W. A., & Sharit, J. (2017).Improving social support for older adults through technology: Findings from the PRISM randomized controlled trial. Gerontologist, 58(3), 467–477. https://doi.org/10.1093/geront/gnw249Google Scholar
Czaja, S. J., Boot, W. R., Charness, N., et al. (2015). The Personalized Reminder Information and Social Management System (PRISM) trial: Rationale, methods and baseline characteristics. Contemporary Clinical Trials, 40, 35–46. https://doi.org/10.1016/j.cct.2014.11.004Google Scholar
Czaja, S. J., Charness, N., Fisk, A. D., et al. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and Aging, 21(2), 333–352. https://doi.org/10.1037/0882-7974.21.2.333Google Scholar
Dijkstra, K., Charness, N., Yordon, R., & Fox, M. (2009). Changes in physiological and self-reported stress in younger and older adults after exposure to a stressful task. Aging, Neuropsychology and Cognition, 16, 338–356. https://doi.org/10.1080/13825580902773859Google Scholar
Dupuy, L., Consel, C., & Sauzéon, H. (2016). Self determination-based design to achieve acceptance of assisted living technologies for older adults. Computers in Human Behavior, 65, 508–521. https://doi.org/10.1016/j.chb.2016.07.042Google Scholar
Durick, J., Robertson, T., Brereton, M., Vetere, F., & Nansen, B. (2013). Dispelling ageing myths in technology design. In Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration (pp. 467–476). New York: ACM.Google Scholar
Fasola, J., & Matarić, M. J. (2013). A socially assistive robot exercise coach for the elderly. Journal of Human-Robot Interaction, 2(2), 3–32. https://doi.org/10.5898/JHRI.2.2.FasolaGoogle Scholar
Grindrod, K. A., Li, M., & Gates, A. (2014). Evaluating user perceptions of mobile medication management applications with older adults: A usability study. JMIR mHealth and uHealth, 2(1), e11. https://doi.org/10.2196/mhealth.3048Google Scholar
Huber, L. L., Shankar, K., Caine, K., et al. (2013). How in-home technologies mediate caregiving relationships in later life. International Journal of Human-Computer Interaction, 29(7), 441–455. https://doi.org/10.1080/10447318.2012.715990Google Scholar
Khosla, R., Chu, M. T., & Nguyen, K. (2013). Enhancing emotional well being of elderly using assistive social robots in Australia. In Proceedings of the 2013 International Conference on Biometrics and Kansei Engineering (ICBAKE) (pp. 41–46). New York: IEEE.Google Scholar
Lee, Y., Lee, J., & Hwang, Y. (2015). Relating motivation to information and communication technology acceptance: Self-determination theory perspective. Computers in Human Behavior, 51, 418–428. https://doi.org/10.1016/j.chb.2015.05.021Google Scholar
Lindenberger, U., Lövdén, M., Schellenbach, M., Li, S. C., & Krüger, A. (2008). Psychological principles of successful aging technologies: A mini-review. Gerontology, 54(1), 59–68. https://doi.org/10.1159/000116114Google Scholar
Lindley, S. E. (2012). Shades of lightweight: Supporting cross-generational communication through home messaging. Universal Access in the Information Society, 11(1), 31–43. https://doi.org/10.1007/s10209-011-0231-2Google Scholar
Ludden, G. D., van Rompay, T. J., Kelders, S. M., & van Gemert-Pijnen, J. E. (2015). How to increase reach and adherence of web-based interventions: A design research viewpoint. Journal of Medical Internet Research, 17(7), e172. https://doi.org/10.2196/jmir.4201Google Scholar
Martinson, M., & Berridge, C. (2015). Successful aging and its discontents: A systematic review of social gerontology literature. Gerontologist, 55, 51–57. https://doi.org/10.1093/geront/gnu037Google Scholar
Matarić, M. J., & Scassellati, B. (2016). Socially assistive robotics. In Springer handbook of robotics (pp. 1973–1994). Cham, Switzerland: Springer.Google Scholar
McGlynn, S. A., Koon, L. M., Blocker, K. A., Shishegar, N., & Rogers, W. A. (2018). Investigating the potential of digital home assistants to promote physical activity and social engagement for older adults. Presented at the Cognitive Aging Conference (CAC), Atlanta, GA, May.Google Scholar
McMahon, S. K., Lewis, B., Oakes, M., et al. (2016). Older adults’ experiences using a commercially available monitor to self-track their physical activity. JMIR mHealth and uHealth, 4(2), e35. https://doi.org/10.2196/mhealth.5120Google Scholar
Mercer, K., Giangregorio, L., Schneider, E., et al. (2016). Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: A mixed-methods evaluation. JMIR mHealth and uHealth, 4(1), e7. https://doi.org/10.2196/mhealth.4225Google Scholar
Mitzner, T. L., Boron, J. B., Fausset, C. B., et al. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26(6), 1710–1721. https://doi.org/10.1016/j.chb.2010.06.020Google Scholar
Mitzner, T. L., Sanford, J. A., & Rogers, W. A. (2018). Closing the capacity-ability gap: Using technology to support aging with disability. Innovation in Aging, 2(1), 1–8. https://doi.org/10.1093/geroni/igy008Google Scholar
Mitzner, T. L., Stuck, R., Hartley, J. Q., Beer, J. M., & Rogers, W. A. (2017). Acceptance of televideo technology by adults aging with a mobility impairment for health and wellness interventions. Journal of Rehabilitation and Assistive Technologies Engineering, 4, 1–2. https://doi.org/10.1177/2055668317692755Google Scholar
Morrow, D. G., & Rogers, W. A. (2008). Environmental support: An integrative framework. Human Factors, 50, 589–613. https://doi.org/10.1518/001872008X312251Google Scholar
Neves, B. B., Franz, R. L., Munteanu, C., Baecker, R., & Ngo, M. (2015). My hand doesn’t listen to me!: Adoption and evaluation of a communication technology for the “oldest old.” In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1593–1602). New York: ACM.Google Scholar
Preusse, K. C., Mitzner, T. L., Fausset, C. B., & Rogers, W. A. (2017). Older adults’ acceptance of activity trackers. Journal of Applied Gerontology, 36(2), 127–155. https://doi.org/10.1177/0733464815624151Google Scholar
Quan-Haase, A., Mo, G. Y., & Wellman, B. (2017). Connected seniors: How older adults in East York exchange social support online and offline. Information, Communication and Society, 20(7), 967–983. https://doi.org/10.1080/1369118X.2017.1305428Google Scholar
Rashidi, P., & Mihailidis, A. (2013). A survey on ambient-assisted living tools for older adults. IEEE Journal of Biomedical and Health Informatics, 17(3), 579–590. https://doi.org/10.1109/JBHI.2012.2234129Google Scholar
Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychological Review, 24, 355–370. https://doi.org/10.1007/s11065-014-9270-9Google Scholar
Rowan, J., & Mynatt, E. D. (2005). Digital family portrait field trial: Support for aging in place. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 521–530). New York: ACM.Google Scholar
Rowe, J. W., & Kahn, R. L. (1987). Human aging: Usual and successful. Science, 237(4811), 143–149. https://doi.org/10.1145/1054972.1055044Google Scholar
Rowe, J. W., & Kahn, R. L. (2015). Successful aging 2.0: Conceptual expansions for the 21st century. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 70(4), 593–596. https://doi.org/10.1093/geronb/gbv025Google Scholar
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68Google Scholar
Seligman, M. E. (2011). Flourish: A visionary new understanding of happiness and well-being. Policy, 27(3), 60–61.Google Scholar
Shen, Z., & Wu, Y. (2016). Investigation of practical use of humanoid robots in elderly care centres. In Proceedings of the Fourth International Conference on Human Agent Interaction (pp. 63–66). New York: ACM.Google Scholar
Shibata, T., & Wada, K. (2011). Robot therapy: A new approach for mental healthcare of the elderly – A mini-review. Gerontology, 57(4), 378–386. https://doi.org/10.1159/000319015Google Scholar
Smarr, C.-A., Mitzner, T. L., Beer, J. M., et al. (2014). Domestic robots for older adults: Attitudes, preferences, and potential. International Journal of Social Robotics, 6(2), 229–247. https://doi.org/10.1007/s12369-013-0220-0Google Scholar
Smith, A., & Anderson, M. (2017). Automation in everyday life. Washington: Pew Research Center.Google Scholar
Souders, D. J., Boot, W. R., Blocker, K., et al. (2017). Evidence for narrow transfer after short-term cognitive training in older adults. Frontiers in Aging Neuroscience, 9, 41. https://doi.org/10.3389/fnagi.2017.00041Google Scholar
Stahl, B. C. (2011). What does the future hold? A critical view of emerging information and communication technologies and their social consequences. In Chiasson, M., Henfridsson, O., Karsten, H., & DeGross, J. I. (Eds.), Researching the future in information systems (pp. 59–76). Berlin: Springer.Google Scholar
Stern, I. (2012). Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurology, 11(11), 1006–1012. https://doi.org/10.1016/S1474-4422(12)70191-6Google Scholar
Stuck, R. E., Chong, A. W., Mitzner, T. L., & Rogers, W. A. (2017). Medication management apps: Usable by older adults? In Proceedings of the Human Factors and Ergonomics Society 61st Annual Meeting (pp. 1141–1144). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
Stuck, R. E., & Rogers, W. A. (2018). Older adults’ perceptions of supporting factors of trust in a robot care provider. Journal of Robotics, 2018, 1–11. https://doi.org/10.1155/2018/6519713Google Scholar
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412Google Scholar
Ware, J. E., Jr., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), 473–483. https://doi.org/10.1097/00005650-199206000-00002Google Scholar
Ziefle, M., Rocker, C., & Holzinger, A. (2011). Medical technology in smart homes: Exploring the user’s perspective on privacy, intimacy and trust. In 2011 IEEE 35th Annual Computer Software and Applications Conference Workshops (COMPSACW) (pp. 410–415). New York: IEEE.Google Scholar

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